Model Inventory Management

Discover and inventory AI models, applications, and services so security teams can govern AI use and prioritize risk review before deployment.

Business impact

• AI asset visibility — The evidence says the platform helped identify and inventory AI models, applications, and services across the environment.
• risk assessment prioritization — The inventory helped the security team prioritize AI risk assessments before deployment.

Data requirements

• AI models — The inventory covered AI models in the environment.
• AI applications — The inventory also covered AI applications.
• AI services — The inventory included AI services across the environment.

AI methods and techniques

• discovery and inventorying — Used to identify AI models, applications, and services across the environment.
• adversarial testing — Used later in the process to simulate attacks and validate AI resilience before deployment.
• continuous monitoring — Used after deployment to observe model behavior and identify suspicious activity.

Industries

Real-world evidence

1 documented case study on record.

Companies using this: NetRise.